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Chang Gao

from Temecula, CA
Age ~50
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Also known as:
Chan G Gao, Chang Goa
Phone and address:
31941 Wildwood Ct, Temecula, CA 92592
(951) 729-9828
Related to:
Yujun Zhao, 51Yuju L Chuang, 57Mingfei Gao, 39Jim Ma, 62Yugao June, 71Jean CaoDong Yuan, 69Tianyi Gao, 33Yu J GaoQuang B GaoGuang Bo Gao, 83Jun Yu, 71Yu TunXiangdong Yu, 61June Y GaoShu Gao, 53Yangfan Gao, 55Mike Gao
Connected to:
Aijian Gao, 42Aiping Gao, 54Aitong Gao, 35Changlu Gao, 52Chenwei Gao, 36Cuifeng Gao, 53Danjie Gao, 39Daphne W Gao, 101Dawen Gao, 56Dongfeng Gao, 65

Chang Gao Phones & Addresses

  • 31941 Wildwood Ct, Temecula, CA 92592 (951) 729-9828
  • Torrance, CA
  • Redondo Beach, CA
  • El Segundo, CA
  • Cerritos, CA
  • Champaign, IL
  • Savoy, IL

Resumes

Resumes

Chang Gao Photo 1

Product Engineer Manager

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Location:
101 north Sepulveda Blvd, El Segundo, CA 90245
Industry:
Semiconductors
Work:
International Rectifier
Product Engineer Manager
International Rectifier 2009 - Dec 2013
Business Operations Manager
International Rectifier Sep 2007 - Dec 2008
Product Evaluation Manager
International Rectifier Jun 2004 - Apr 2007
Failure Analysis Engineer
Education:
University of Illinois at Urbana - Champaign 2001 - 2004
Bachelors, Bachelor of Science
Skills:
Semiconductors
Semiconductor Industry
Ic
Electronics
Failure Analysis
Design of Experiments
Mixed Signal
Cmos
Engineering Management
Product Engineering
Analog
Simulations
Spc
Jmp
Asic
Silicon
Integrated Circuits
Statistical Process Control
Chang Gao Photo 2

Chang Gao

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Publications

Us Patents

Method And System For Accelerated Acquisition And Artifact Reduction Of Undersampled Mri Using A Deep Learning Based 3D Generative Adversarial Network

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US Patent:
20220381861, Dec 1, 2022
Filed:
May 19, 2021
Appl. No.:
17/324161
Inventors:
- Erlangen, DE
- Oakland CA, US
Chang Gao - Los Angeles CA, US
Valid Ghodrati - Glendale CA, US
International Classification:
G01R 33/565
G01R 33/48
G01R 33/56
G06N 20/00
G06K 9/62
Abstract:
Systems and methods for generative adversarial networks (GANs) to remove artifacts from undersampled magnetic resonance (MR) images are described. The process of training the GAN can include providing undersampled 3D MR images to the generator model, providing the generated example and a real example to the discriminator model, applying adversarial loss, L2 loss, and structural similarity index measure loss to the generator model based on a classification output by the discriminator model, and repeating until the generator model has been trained to remove the artifacts from the undersampled 3D MR images. At runtime, the trained generator model of the GAN can be generate artifact-free images or parameter maps from undersampled MRI data of a patient.
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Chang Gao from Temecula, CA, age ~50 Get Report